A recommendation system is, for example, a system that performs clustering of customers from preference of the customers, and presents products highly likely to be purchased. Note that, although clustering is actually performed of customer IDs and product IDs, here, the clustering may be described as “perform clustering of customers”, and “perform clustering of products”, for convenience. The recommendation system can be applied to various trading activities performed inside and outside the Internet.
A model that performs clustering of customers and products from a purchase history and quantifies a possibility of purchasing is used in a general recommendation system. As a typical example of such a model, there are collaborative filtering and relational models. In the relational models, a Stochastic Block Model and a Mixed Membership Stochastic Block Model are often used. In the Stochastic Block Model and the Mixed Membership Stochastic Block Model, strength of relationship between a customer cluster and a product cluster (purchase tendency, for example) is quantified with a real number in a range from 0 to 1. For that reason, a product belonging to a product cluster strongly related to a customer cluster is recommended to a customer belonging to the customer cluster. Note that, it can be said that the purchase history is relational data indicating a relationship between products and customers.
A recommendation algorithm using collaborative filtering is described in NPL 1.
A relational topic model combining a relational model and a topic model is described in NPL 2. In the relational topic model, it is assumed that textual data is associated with all clustering targets.